Conventional and associative memory-based spelling checkers

dc.contributor.author	Cherkassky, Vladimir	en
dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Brodt, G.	en
dc.date.accessioned	2015-05-12T20:36:56Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10262
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=130323&abstractAccess=no&userType=inst	en
dc.subject	βάσεις δεδομένων
dc.subject	Ανάκτηση δεδομένων
dc.subject	μνήμη
dc.subject	Συμβατική και συνειρμική
dc.subject	Έλεγχος ορθογραφίας
dc.subject	Databases
dc.subject	Information retrieval
dc.subject	memory
dc.subject	Conventional and associative
dc.title	Conventional and associative memory-based spelling checkers	en
heal.type	conferenceItem
heal.generalDescription	σε έντυπη μορφή στο γραφείο μου	el
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classification	Technology
heal.classification	Computer science
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh86007767
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85066148
heal.identifier.secondary	DOI: 10.1109/TAI.1990.130323
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1990-11-06
heal.bibliographicCitation	Cherkassky, V., Vassilas, N. and Brodt, G. (1990) Conventional and associative memory-based spelling checkers. Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence. pp.138-144. Herndon: IEEE	en
heal.abstract	Conventional and emerging neural approaches to fault-tolerant data retrieval when the input keyword and/or database itself may contain noise (errors) are reviewed. Spelling checking is used as a primary example to illustrate various approaches and to contrast the difference between conventional (algorithmic) techniques and research methods based on neural associative memories. Recent research on associative spelling checkers is summarized and some original results are presented. It is concluded that most neural models do not provide a viable solution for robust data retrieval due to saturation and scaling problems. However, a combination of conventional and neural approaches is shown to have excellent error correction rates and low computational costs; hence, it can be a good choice for robust data retrieval in large databases.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence	en
heal.conferenceItemType	full paper